This article outlines how Almanac can be used to assist supply chain management, by forecasting demand in two retail areas.
↳ Who’s this guide for?
This guide is for users working within store operations who are involved in the allocation of stock.
If you need to make sure you’re not over-stocking areas with low demand, or under-stocking areas with high demand - this guide is for you.
1. Select your Chain
Above: The search modal for Chains
Start by clicking Chains from the navigation bar to open up the search modal for Chains.
↳ Chain search
Use the search box on the side panel to search by Chain name. When you find your Chain, select it from the list of results. At this point the USA will be highlighted on the map.
If you’re looking to compare demand between states or cities, you can use the search box on the map to refine your analysis to that location. If this is the case, you can skip step 2 and go straight to step 3.
💡Tip: If you’re selecting a city, make sure all of your stores are covered. In Almanac, city boundaries as designated by the US Census Bureau are used. These boundaries may not align to your own definitions.
If, on the other hand, you’re looking to analyze a more specific group of stores - simply click the ‘ANALYZE’ button when the USA is highlighted and continue on to step 2.
2. Create your groups
Above: An analysis page for Starbucks
You’re now on a Chain analysis page for your Chain. This page outlines nationwide performance, so let’s create a custom group of the stores that you’re interested in.
Above: The menu to create a custom group
From here click the ⋮ icon on the right hand side of the header menu and click ‘Create Group’.
Above: The create group modal lets you create a custom analysis
↳ Custom Groups
A modal will open with instructions on how to create a custom group of Places. Start by entering a name for this group.
Above: To save a group, you need to give it a name
Next you can add group rules. These are dynamic rules, so if new stores open after you create the group, they will be added as long as they meet them. There are two types of rules:
Sub-chains
Locations
Above: Some Chains, like Walmart, can create groups based on sub-chain
Sub-chain rules
If your Chain has different types of stores that are evident through the store’s name, you can include or exclude certain sub-chains. For example, Walmart’s sub-chains include Supercenters and Neighborhood Markets.
⚠️ Note: If all of your stores have the same name, this section will not appear. If this is a mistake, reach out to support@passby.com and we can update accordingly.
Above: All Chains can create groups based on location
Location rules
Every Chain will be able to set rules based on location. You can add rules based on the state, city and ZIP code that the stores are in, or add individual stores by adding the street address.
⚠️ Note: If a location does not appear when you are searching for it, this is likely because we do not have a record for stores in that area. Reach out at support@passby.com and we can add it to our data.
Above: Pinpoints indicating included stores will appear when you have added rules
Once you have finished selecting your rules, click the ‘SAVE AND GO TO GROUP’ button.
💡Tip: If you want to edit or delete a group after creating one, you can do so from the Settings section. Click the profile icon in the top right corner of the screen.
3. Analyze consumer demand
Above: An analysis page for a custom group of Starbucks restaurants
You will now be on a Chain analysis page for your custom group (or for a state or city if you skipped step 2). From here you can analyze overall demand to these stores with the Forecasted Visits analytic.
Above: The Forecasted Visits analytic, displaying the next 30 day’s worth of foot traffic
↳ Forecasted Visits
Scroll down to the Forecasted visits analytic. On this page, the analytic provides a 12 week forecast on the overall demand for all stores. For the next 30 days you can also see an hourly forecast by selecting ‘Hourly’ on the aggregation selector.
Use the dropdown above the graph to select the start and end dates. Adjust to the aggregation you want to suit your needs.
💡Tip: If there’s a sharp drop at the start or end of your date range, make sure the dates align to the aggregation. Weeks begin on Sundays and end on Saturdays; Months are based on calendar months; and quarters match the calendar year
(i.e. Q1 is January to March).
4. Export the data
Above: The Forecasted Visits analytic, displaying 14 days worth of hourly foot traffic
When you’re happy with the presentation of the data, select the export button next to the End Date picker. This will enable you to download the data as a .csv.
Above: The Export Data dialog box
A dialog box will open so that you can download the data. Simply enter a filename and click ‘Export’ to download as a .csv.
💡Tip: If you’d like to receive a data feed as well as your Almanac access, reach out to your account manager or to customersuccess@passby.com.
5. Compare the data
Repeat steps 2-4 to access data on the second group of stores.
At this point you’ll have two datasets - one for each of the groups of stores you’ve created.
You can use this to:
Compare demand for specific dates
Compare the trend of demand for each group
Combine with your first party data to create a personalized model
↳ Summary
So now you know how to use Almanac forecast demand in two groups of stores. You’ve learnt how to create a custom group of stores for a personalized analysis, how to use the Forecasted Visits analytic to predict demand, and how to export the data to combine with your own data.
Now you’ve got the data to help you allocate stock. Great job!